In recent years, various types of advanced driver assistance systems (ADAS) have been developed for the convenience and safety of drivers. For example, various systems, such as a smart cruise control (SCC) system that automatically adjusts the vehicle speed to maintain a safe distance from vehicles ahead, a lane keeping assist system (LKAS) that helps keep a vehicle into its lane through control of steering when a driver leaves, or nearly leaves, the lane without turn signal activation, and a smart parking assist system (SPAS) that automatically parks a vehicle instead of a driver by recognizing a parking space, are being applied to vehicles. Further, the applications and functions thereof are gradually being expanded. Such systems may be provided with various types of sensors, such as a radio detection and ranging (RADAR) sensor, a light detection and ranging (LiDAR) sensor, a camera, and an ultrasonic sensor, so as to recognize a driving environment and provide assistance in accordance with driving conditions.
ADAS performs auxiliary controls with respect to acceleration and deceleration, steering, and the like of vehicles on the basis of information about a surrounding environment, such as a distance and a speed obtained by sensors. Therefore, the calibration of errors by determining whether or not the sensors have malfunctioned in real time may be essential for ADAS.
However, high-priced evaluation equipment and calibration equipment is required in order to implement the calibration of errors by determining whether or not the sensors have malfunctioned.